Machine Learning on Camera Images for Fast mmWave Beamforming
Machine Learning on Camera Images for Fast mmWave Beamforming
复制标题
DOI：
10.1109/mass50613.2020.00049
复制
发表时间：
2020-12
期刊：
影响因子：
--
通讯作者：
Batool Salehi;M. Belgiovine;Saray Sanchez;Jennifer G. Dy;Stratis Ioannidis;K. Chowdhury
中科院分区：
文献类型：
--
作者：
Batool Salehi;M. Belgiovine;Saray Sanchez;Jennifer G. Dy;Stratis Ioannidis;K. Chowdhury
在毫米波频段中进行波束成形，需要在发射和接收节点的选定波束扇区中进行完美对准。当前的802.11ad WiFi和新兴的5G蜂窝标准花费了长达几毫秒的时间来探索不同的扇区组合，以识别具有最高SNR的波束对。本文提出了一种基于两个连续卷积神经网络(CNN)的机器学习方法，该方法使用摄像机图像形式的带外信息来(I)快速识别发送器和接收器节点的位置，然后(Ii)返回最优波束对。我们使用NI 60 GHz毫米波收发器对这一有趣的室内设置概念进行了实验验证。结果表明，在不同的环境光照条件下，我们的最大似然方法将波束形成相关的探测时间减少了93%，与当前标准定义的时间密集型确定性方法相比，误差不到1%。
Perfect alignment in chosen beam sectors at both transmit- and receive-nodes is required for beamforming in mmWave bands. Current 802.11ad WiFi and emerging 5G cellular standards spend up to several milliseconds exploring different sector combinations to identify the beam pair with the highest SNR. In this paper, we propose a machine learning (ML) approach with two sequential convolutional neural networks (CNN) that uses out-of-band information, in the form of camera images, to (i) rapidly identify the locations of the transmitter and receiver nodes, and then (ii) return the optimal beam pair. We experimentally validate this intriguing concept for indoor settings using the NI 60GHz mmwave transceiver. Our results reveal that our ML approach reduces beamforming related exploration time by 93% under different ambient lighting conditions, with an error of less than 1% compared to the time-intensive deterministic method defined by the current standards.